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cs.MA2026
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection
Jan Stenner, Hans Harder, Sebastian Peitz
This paper studies sparse sensor placement for control of Rayleigh-Bénard convection with multi-agent reinforcement learning. We train dense expert policies with windowed observati…
cs.LG2026
Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection
Tim Plotzki, Sebastian Peitz
Training reinforcement learning (RL) agents to control fluid dynamics systems is computationally expensive due to the high cost of direct numerical simulations (DNS) of the governi…
cs.LG2026
Automatic feature identification in least-squares policy iteration using the Koopman operator framework
Christian Mugisho Zagabe, Sebastian Peitz
In this paper, we present a Koopman autoencoder-based least-squares policy iteration (KAE-LSPI) algorithm in reinforcement learning (RL). The KAE-LSPI algorithm is based on reformu…